Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations440833
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.4 MiB
Average record size in memory96.0 B

Variable types

Numeric8
Categorical4

Alerts

Churn is highly overall correlated with CustomerID and 1 other fieldsHigh correlation
CustomerID is highly overall correlated with ChurnHigh correlation
Support Calls is highly overall correlated with ChurnHigh correlation
CustomerID has unique values Unique

Reproduction

Analysis started2025-06-16 20:52:07.195326
Analysis finished2025-06-16 20:52:16.198900
Duration9 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CustomerID
Real number (ℝ)

High correlation  Unique 

Distinct440833
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2791391 × 10-16
Minimum-1.7400899
Maximum1.733942
Zeros0
Zeros (%)0.0%
Negative219713
Negative (%)49.8%
Memory size3.4 MiB
2025-06-16T21:52:16.258748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7400899
5-th percentile-1.569872
Q1-0.86292957
median0.0056112196
Q30.8672811
95-th percentile1.5479365
Maximum1.733942
Range3.4740319
Interquartile range (IQR)1.7302107

Descriptive statistics

Standard deviation1.0000011
Coefficient of variation (CV)-7.8177671 × 1015
Kurtosis-1.2006398
Mean-1.2791391 × 10-16
Median Absolute Deviation (MAD)0.86503586
Skewness-0.018485883
Sum-1.7447221 × 10-10
Variance1.0000023
MonotonicityNot monotonic
2025-06-16T21:52:16.338690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.740089882 1
 
< 0.1%
0.5837364471 1
 
< 0.1%
0.5837210068 1
 
< 0.1%
0.5837132867 1
 
< 0.1%
0.5837055666 1
 
< 0.1%
0.5836978465 1
 
< 0.1%
0.5836901263 1
 
< 0.1%
0.5836824062 1
 
< 0.1%
0.5836746861 1
 
< 0.1%
0.583666966 1
 
< 0.1%
Other values (440823) 440823
> 99.9%
ValueCountFrequency (%)
-1.740089882 1
< 0.1%
-1.740082162 1
< 0.1%
-1.740074442 1
< 0.1%
-1.740066722 1
< 0.1%
-1.740059002 1
< 0.1%
-1.740043562 1
< 0.1%
-1.740035841 1
< 0.1%
-1.740028121 1
< 0.1%
-1.740020401 1
< 0.1%
-1.740012681 1
< 0.1%
ValueCountFrequency (%)
1.733942046 1
< 0.1%
1.733934326 1
< 0.1%
1.733926606 1
< 0.1%
1.733918886 1
< 0.1%
1.733911166 1
< 0.1%
1.733903445 1
< 0.1%
1.733895725 1
< 0.1%
1.733888005 1
< 0.1%
1.733880285 1
< 0.1%
1.733872565 1
< 0.1%

Age
Real number (ℝ)

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4854518 × 10-16
Minimum-1.7177758
Maximum2.0596483
Zeros0
Zeros (%)0.0%
Negative221989
Negative (%)50.4%
Memory size3.4 MiB
2025-06-16T21:52:16.460297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7177758
5-th percentile-1.5570343
Q1-0.83369781
median-0.029990549
Q30.69334598
95-th percentile1.7381654
Maximum2.0596483
Range3.7774241
Interquartile range (IQR)1.5270438

Descriptive statistics

Standard deviation1.0000011
Coefficient of variation (CV)6.7319661 × 1015
Kurtosis-0.86484848
Mean1.4854518 × 10-16
Median Absolute Deviation (MAD)0.72333653
Skewness0.16201607
Sum6.7302608 × 10-11
Variance1.0000023
MonotonicityNot monotonic
2025-06-16T21:52:16.540436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.8540874355 13527
 
3.1%
0.2111216286 12578
 
2.9%
0.05038017692 12417
 
2.8%
0.6933459838 12379
 
2.8%
0.6129752579 12369
 
2.8%
0.5326045321 12368
 
2.8%
0.3718630804 12344
 
2.8%
0.7737167097 12331
 
2.8%
0.1307509028 12314
 
2.8%
0.2914923545 12298
 
2.8%
Other values (38) 315908
71.7%
ValueCountFrequency (%)
-1.717775792 8219
1.9%
-1.637405066 8073
1.8%
-1.55703434 9553
2.2%
-1.476663614 9574
2.2%
-1.396292889 9639
2.2%
-1.315922163 9513
2.2%
-1.235551437 9465
2.1%
-1.155180711 9647
2.2%
-1.074809985 9692
2.2%
-0.9944392593 9472
2.1%
ValueCountFrequency (%)
2.059648323 5460
1.2%
1.979277598 5496
1.2%
1.898906872 5560
1.3%
1.818536146 5288
1.2%
1.73816542 5407
1.2%
1.657794694 5430
1.2%
1.577423968 5573
1.3%
1.497053242 5373
1.2%
1.416682517 5361
1.2%
1.336311791 5477
1.2%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.2 MiB
0.8726679277343465
250253 
-1.145911254692536
190580 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters7934994
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.145911254692536
2nd row-1.145911254692536
3rd row-1.145911254692536
4th row0.8726679277343465
5th row0.8726679277343465

Common Values

ValueCountFrequency (%)
0.8726679277343465 250253
56.8%
-1.145911254692536 190580
43.2%

Length

2025-06-16T21:52:16.616993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-16T21:52:16.663034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.8726679277343465 250253
56.8%
1.145911254692536 190580
43.2%

Most occurring characters

ValueCountFrequency (%)
6 1131919
14.3%
7 1001012
12.6%
2 881666
11.1%
4 881666
11.1%
5 821993
10.4%
1 762320
9.6%
3 691086
8.7%
9 631413
8.0%
. 440833
 
5.6%
0 250253
 
3.2%
Other values (2) 440833
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7934994
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 1131919
14.3%
7 1001012
12.6%
2 881666
11.1%
4 881666
11.1%
5 821993
10.4%
1 762320
9.6%
3 691086
8.7%
9 631413
8.0%
. 440833
 
5.6%
0 250253
 
3.2%
Other values (2) 440833
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7934994
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 1131919
14.3%
7 1001012
12.6%
2 881666
11.1%
4 881666
11.1%
5 821993
10.4%
1 762320
9.6%
3 691086
8.7%
9 631413
8.0%
. 440833
 
5.6%
0 250253
 
3.2%
Other values (2) 440833
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7934994
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 1131919
14.3%
7 1001012
12.6%
2 881666
11.1%
4 881666
11.1%
5 821993
10.4%
1 762320
9.6%
3 691086
8.7%
9 631413
8.0%
. 440833
 
5.6%
0 250253
 
3.2%
Other values (2) 440833
 
5.6%

Tenure
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.7136296 × 10-17
Minimum-1.7534124
Maximum1.66575
Zeros0
Zeros (%)0.0%
Negative218104
Negative (%)49.5%
Memory size3.4 MiB
2025-06-16T21:52:16.735327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7534124
5-th percentile-1.5795567
Q1-0.88413381
median0.043096662
Q30.85442333
95-th percentile1.5498462
Maximum1.66575
Range3.4191624
Interquartile range (IQR)1.7385571

Descriptive statistics

Standard deviation1.0000011
Coefficient of variation (CV)-2.6927864 × 1016
Kurtosis-1.1925189
Mean-3.7136296 × 10-17
Median Absolute Deviation (MAD)0.86927857
Skewness-0.061401972
Sum-6.5483619 × 10-11
Variance1.0000023
MonotonicityNot monotonic
2025-06-16T21:52:16.793602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04309666205 7829
 
1.8%
1.028279039 7815
 
1.8%
1.433942371 7812
 
1.8%
1.375990467 7777
 
1.8%
0.1010485666 7770
 
1.8%
1.202134753 7769
 
1.8%
-0.07280714705 7750
 
1.8%
0.9123752303 7747
 
1.8%
0.9703271348 7737
 
1.8%
1.491894276 7735
 
1.8%
Other values (50) 363092
82.4%
ValueCountFrequency (%)
-1.753412379 6407
1.5%
-1.695460474 6575
1.5%
-1.63750857 6417
1.5%
-1.579556665 6606
1.5%
-1.521604761 6669
1.5%
-1.463652856 7704
1.7%
-1.405700952 7569
1.7%
-1.347749047 7670
1.7%
-1.289797143 7534
1.7%
-1.231845238 7674
1.7%
ValueCountFrequency (%)
1.665749989 7658
1.7%
1.607798085 7597
1.7%
1.54984618 7669
1.7%
1.491894276 7735
1.8%
1.433942371 7812
1.8%
1.375990467 7777
1.8%
1.318038562 7606
1.7%
1.260086658 7665
1.7%
1.202134753 7769
1.8%
1.144182848 7594
1.7%

Usage Frequency
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.7767571 × 10-17
Minimum-1.7245645
Maximum1.6529395
Zeros0
Zeros (%)0.0%
Negative214152
Negative (%)48.6%
Memory size3.4 MiB
2025-06-16T21:52:16.880305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7245645
5-th percentile-1.6080988
Q1-0.79283926
median0.022420338
Q30.83767993
95-th percentile1.5364739
Maximum1.6529395
Range3.377504
Interquartile range (IQR)1.6305192

Descriptive statistics

Standard deviation1.0000011
Coefficient of variation (CV)-1.731077 × 1016
Kurtosis-1.1758107
Mean-5.7767571 × 10-17
Median Absolute Deviation (MAD)0.81525959
Skewness-0.04347368
Sum-4.56295 × 10-11
Variance1.0000023
MonotonicityNot monotonic
2025-06-16T21:52:16.938828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
-0.5599079429 15311
 
3.5%
1.536473869 15284
 
3.5%
0.4882829633 15258
 
3.5%
1.070611244 15237
 
3.5%
1.652939526 15232
 
3.5%
0.6047486195 15205
 
3.4%
0.371817307 15204
 
3.4%
-0.4434422867 15179
 
3.4%
1.187076901 15134
 
3.4%
-0.09404531796 15129
 
3.4%
Other values (20) 288660
65.5%
ValueCountFrequency (%)
-1.724564505 13797
3.1%
-1.608098849 13633
3.1%
-1.491633193 13843
3.1%
-1.375167537 13549
3.1%
-1.25870188 13716
3.1%
-1.142236224 13746
3.1%
-1.025770568 13555
3.1%
-0.9093049117 13725
3.1%
-0.7928392554 13770
3.1%
-0.6763735992 15090
3.4%
ValueCountFrequency (%)
1.652939526 15232
3.5%
1.536473869 15284
3.5%
1.420008213 15012
3.4%
1.303542557 15121
3.4%
1.187076901 15134
3.4%
1.070611244 15237
3.5%
0.9541455882 15038
3.4%
0.837679932 15072
3.4%
0.7212142757 15005
3.4%
0.6047486195 15205
3.4%

Support Calls
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.650502 × 10-17
Minimum-1.1740024
Maximum2.0831026
Zeros0
Zeros (%)0.0%
Negative258652
Negative (%)58.7%
Memory size3.4 MiB
2025-06-16T21:52:16.984693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.1740024
5-th percentile-1.1740024
Q1-0.84829192
median-0.19687091
Q30.78026061
95-th percentile2.0831026
Maximum2.0831026
Range3.2571051
Interquartile range (IQR)1.6285525

Descriptive statistics

Standard deviation1.0000011
Coefficient of variation (CV)-6.0587695 × 1016
Kurtosis-0.74590605
Mean-1.650502 × 10-17
Median Absolute Deviation (MAD)0.65142102
Skewness0.6668105
Sum2.6596725 × 10-11
Variance1.0000023
MonotonicityNot monotonic
2025-06-16T21:52:17.032191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-1.174002433 69875
15.9%
-0.8482919248 69476
15.8%
-0.5225814169 66571
15.1%
-0.1968709089 52730
12.0%
0.128839599 38750
8.8%
0.454550107 24918
 
5.7%
2.083102647 23900
 
5.4%
1.105971123 23870
 
5.4%
1.757392139 23630
 
5.4%
1.431681631 23559
 
5.3%
ValueCountFrequency (%)
-1.174002433 69875
15.9%
-0.8482919248 69476
15.8%
-0.5225814169 66571
15.1%
-0.1968709089 52730
12.0%
0.128839599 38750
8.8%
0.454550107 24918
 
5.7%
0.7802606149 23554
 
5.3%
1.105971123 23870
 
5.4%
1.431681631 23559
 
5.3%
1.757392139 23630
 
5.4%
ValueCountFrequency (%)
2.083102647 23900
 
5.4%
1.757392139 23630
 
5.4%
1.431681631 23559
 
5.3%
1.105971123 23870
 
5.4%
0.7802606149 23554
 
5.3%
0.454550107 24918
 
5.7%
0.128839599 38750
8.8%
-0.1968709089 52730
12.0%
-0.5225814169 66571
15.1%
-0.8482919248 69476
15.8%

Payment Delay
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8883786 × 10-17
Minimum-1.5700715
Maximum2.0627501
Zeros0
Zeros (%)0.0%
Negative220631
Negative (%)50.0%
Memory size3.4 MiB
2025-06-16T21:52:17.080032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.5700715
5-th percentile-1.4489775
Q1-0.84350719
median-0.11694288
Q30.73071548
95-th percentile1.820562
Maximum2.0627501
Range3.6328216
Interquartile range (IQR)1.5742227

Descriptive statistics

Standard deviation1.0000011
Coefficient of variation (CV)3.462154 × 1016
Kurtosis-0.89567331
Mean2.8883786 × 10-17
Median Absolute Deviation (MAD)0.72656431
Skewness0.26740821
Sum5.8251182 × 10-12
Variance1.0000023
MonotonicityNot monotonic
2025-06-16T21:52:17.133539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
-0.1169428808 17199
 
3.9%
-0.2380369329 17185
 
3.9%
0.8518095362 17175
 
3.9%
0.004151171341 17095
 
3.9%
0.1252452235 17078
 
3.9%
-0.359130985 17051
 
3.9%
0.609621432 17027
 
3.9%
-0.7224131414 17027
 
3.9%
-1.20678935 17025
 
3.9%
-1.448977454 17021
 
3.9%
Other values (21) 269950
61.2%
ValueCountFrequency (%)
-1.570071506 16904
3.8%
-1.448977454 17021
3.9%
-1.327883402 16822
3.8%
-1.20678935 17025
3.9%
-1.085695298 16938
3.8%
-0.9646012456 16744
3.8%
-0.8435071935 16954
3.8%
-0.7224131414 17027
3.9%
-0.6013190893 16892
3.8%
-0.4802250372 16869
3.8%
ValueCountFrequency (%)
2.062750057 8590
1.9%
1.941656005 8446
1.9%
1.820561953 8299
1.9%
1.699467901 8178
1.9%
1.578373849 8383
1.9%
1.457279797 8362
1.9%
1.336185745 8325
1.9%
1.215091693 8323
1.9%
1.09399764 8454
1.9%
0.9729035883 8670
2.0%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.4 MiB
1.2115426547251789
149129 
-0.0170083611018051
148678 
-1.2455593769287892
143026 

Length

Max length19
Median length19
Mean length18.661711
Min length18

Characters and Unicode

Total characters8226698
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.2115426547251789
2nd row-1.2455593769287892
3rd row-1.2455593769287892
4th row1.2115426547251789
5th row-1.2455593769287892

Common Values

ValueCountFrequency (%)
1.2115426547251789 149129
33.8%
-0.0170083611018051 148678
33.7%
-1.2455593769287892 143026
32.4%

Length

2025-06-16T21:52:17.183901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-16T21:52:17.218317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.2115426547251789 149129
33.8%
0.0170083611018051 148678
33.7%
1.2455593769287892 143026
32.4%

Most occurring characters

ValueCountFrequency (%)
1 1482932
18.0%
5 1025143
12.5%
0 892068
10.8%
2 876465
10.7%
7 732988
8.9%
8 732537
8.9%
9 578207
 
7.0%
4 441284
 
5.4%
. 440833
 
5.4%
6 440833
 
5.4%
Other values (2) 583408
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8226698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1482932
18.0%
5 1025143
12.5%
0 892068
10.8%
2 876465
10.7%
7 732988
8.9%
8 732537
8.9%
9 578207
 
7.0%
4 441284
 
5.4%
. 440833
 
5.4%
6 440833
 
5.4%
Other values (2) 583408
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8226698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1482932
18.0%
5 1025143
12.5%
0 892068
10.8%
2 876465
10.7%
7 732988
8.9%
8 732537
8.9%
9 578207
 
7.0%
4 441284
 
5.4%
. 440833
 
5.4%
6 440833
 
5.4%
Other values (2) 583408
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8226698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1482932
18.0%
5 1025143
12.5%
0 892068
10.8%
2 876465
10.7%
7 732988
8.9%
8 732537
8.9%
9 578207
 
7.0%
4 441284
 
5.4%
. 440833
 
5.4%
6 440833
 
5.4%
Other values (2) 583408
 
7.1%

Contract Length
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.2 MiB
-1.1146608840057235
177199 
1.118049203502092
176530 
0.0016941597481843
87104 

Length

Max length19
Median length18
Mean length18.001518
Min length17

Characters and Unicode

Total characters7935663
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.1146608840057235
2nd row0.0016941597481843
3rd row1.118049203502092
4th row0.0016941597481843
5th row0.0016941597481843

Common Values

ValueCountFrequency (%)
-1.1146608840057235 177199
40.2%
1.118049203502092 176530
40.0%
0.0016941597481843 87104
19.8%

Length

2025-06-16T21:52:17.269051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-16T21:52:17.299636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.1146608840057235 177199
40.2%
1.118049203502092 176530
40.0%
0.0016941597481843 87104
19.8%

Most occurring characters

ValueCountFrequency (%)
0 1499029
18.9%
1 1322499
16.7%
4 792240
10.0%
2 706789
8.9%
8 705136
8.9%
5 618032
7.8%
9 527268
 
6.6%
6 441502
 
5.6%
. 440833
 
5.6%
3 440833
 
5.6%
Other values (2) 441502
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7935663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1499029
18.9%
1 1322499
16.7%
4 792240
10.0%
2 706789
8.9%
8 705136
8.9%
5 618032
7.8%
9 527268
 
6.6%
6 441502
 
5.6%
. 440833
 
5.6%
3 440833
 
5.6%
Other values (2) 441502
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7935663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1499029
18.9%
1 1322499
16.7%
4 792240
10.0%
2 706789
8.9%
8 705136
8.9%
5 618032
7.8%
9 527268
 
6.6%
6 441502
 
5.6%
. 440833
 
5.6%
3 440833
 
5.6%
Other values (2) 441502
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7935663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1499029
18.9%
1 1322499
16.7%
4 792240
10.0%
2 706789
8.9%
8 705136
8.9%
5 618032
7.8%
9 527268
 
6.6%
6 441502
 
5.6%
. 440833
 
5.6%
3 440833
 
5.6%
Other values (2) 441502
 
5.6%

Total Spend
Real number (ℝ)

Distinct68363
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0934576 × 10-16
Minimum-2.2076863
Maximum1.5298171
Zeros0
Zeros (%)0.0%
Negative201409
Negative (%)45.7%
Memory size3.4 MiB
2025-06-16T21:52:17.340278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.2076863
5-th percentile-1.8879221
Q1-0.62962932
median0.12202413
Q30.82384421
95-th percentile1.3881823
Maximum1.5298171
Range3.7375034
Interquartile range (IQR)1.4534735

Descriptive statistics

Standard deviation1.0000011
Coefficient of variation (CV)9.1453124 × 1015
Kurtosis-0.75148338
Mean1.0934576 × 10-16
Median Absolute Deviation (MAD)0.71656245
Skewness-0.4571754
Sum-1.7073898 × 10-11
Variance1.0000023
MonotonicityNot monotonic
2025-06-16T21:52:17.406997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.651213576 269
 
0.1%
-0.8289628366 267
 
0.1%
0.2964409536 266
 
0.1%
0.8861359286 265
 
0.1%
-0.2101983911 265
 
0.1%
-2.04572782 265
 
0.1%
-2.070644509 263
 
0.1%
-0.4053791222 262
 
0.1%
-0.07730938262 262
 
0.1%
-1.505866223 261
 
0.1%
Other values (68353) 438188
99.4%
ValueCountFrequency (%)
-2.207686299 100
< 0.1%
-2.207603244 1
 
< 0.1%
-2.207437132 1
 
< 0.1%
-2.207395605 1
 
< 0.1%
-2.207354077 1
 
< 0.1%
-2.207312549 2
 
< 0.1%
-2.207229493 1
 
< 0.1%
-2.207187965 2
 
< 0.1%
-2.207146438 2
 
< 0.1%
-2.207021854 3
 
< 0.1%
ValueCountFrequency (%)
1.529817063 111
< 0.1%
1.529775535 5
 
< 0.1%
1.529734008 2
 
< 0.1%
1.52969248 3
 
< 0.1%
1.529650952 7
 
< 0.1%
1.529609424 2
 
< 0.1%
1.529567896 5
 
< 0.1%
1.529526369 4
 
< 0.1%
1.529484841 6
 
< 0.1%
1.529443313 2
 
< 0.1%

Last Interaction
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8799135 × 10-17
Minimum-1.5682378
Maximum1.8053505
Zeros0
Zeros (%)0.0%
Negative233646
Negative (%)53.0%
Memory size3.4 MiB
2025-06-16T21:52:17.453905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.5682378
5-th percentile-1.4519071
Q1-0.87025397
median-0.05593955
Q30.8747055
95-th percentile1.6890199
Maximum1.8053505
Range3.3735883
Interquartile range (IQR)1.7449595

Descriptive statistics

Standard deviation1.0000011
Coefficient of variation (CV)1.7007072 × 1016
Kurtosis-1.1537555
Mean5.8799135 × 10-17
Median Absolute Deviation (MAD)0.81431442
Skewness0.17677463
Sum1.9442559 × 10-11
Variance1.0000023
MonotonicityNot monotonic
2025-06-16T21:52:17.498102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
-0.8702539682 16914
 
3.8%
-0.05593955045 16772
 
3.8%
-0.7539233371 16762
 
3.8%
0.06039108066 16750
 
3.8%
-0.9865845993 16746
 
3.8%
-1.568237755 16727
 
3.8%
-0.2886008127 16722
 
3.8%
-1.335576493 16711
 
3.8%
-1.10291523 16710
 
3.8%
-0.5212620749 16685
 
3.8%
Other values (20) 273334
62.0%
ValueCountFrequency (%)
-1.568237755 16727
3.8%
-1.451907124 16663
3.8%
-1.335576493 16711
3.8%
-1.219245862 16570
3.8%
-1.10291523 16710
3.8%
-0.9865845993 16746
3.8%
-0.8702539682 16914
3.8%
-0.7539233371 16762
3.8%
-0.637592706 16532
3.8%
-0.5212620749 16685
3.8%
ValueCountFrequency (%)
1.805350547 12654
2.9%
1.689019916 12567
2.9%
1.572689285 12754
2.9%
1.456358654 12787
2.9%
1.340028023 12823
2.9%
1.223697392 12603
2.9%
1.107366761 12893
2.9%
0.9910361295 12644
2.9%
0.8747054984 12690
2.9%
0.7583748673 12645
2.9%

Churn
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.9 MiB
1.0
250000 
0.0
190833 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1322499
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 250000
56.7%
0.0 190833
43.3%

Length

2025-06-16T21:52:17.538953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-16T21:52:17.573539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 250000
56.7%
0.0 190833
43.3%

Most occurring characters

ValueCountFrequency (%)
0 631666
47.8%
. 440833
33.3%
1 250000
 
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1322499
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 631666
47.8%
. 440833
33.3%
1 250000
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1322499
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 631666
47.8%
. 440833
33.3%
1 250000
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1322499
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 631666
47.8%
. 440833
33.3%
1 250000
 
18.9%

Interactions

2025-06-16T21:52:15.109890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:11.592057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.120995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.755020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.200866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.651954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.117595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.556991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:15.161999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:11.675682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.354009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.810405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.254797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.707421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.177131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.614714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:15.219434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:11.768736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.409277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.864335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.307335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.765092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.237174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.668825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:15.272055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:11.835388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.463944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.916928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.359560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.821340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.289218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.720178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:15.333291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:11.903112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.518375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.984975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.416665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.880710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.342314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.885600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:15.385962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:11.952872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.578463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.038148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.473625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.938678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.396275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.939103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:15.441782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.004142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.637282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.092938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.532968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.999701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.449456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.996486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:15.498536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.056005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:12.694931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.147950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:13.591230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.055190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:14.503598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-16T21:52:15.054868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-16T21:52:17.617170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeChurnContract LengthCustomerIDGenderLast InteractionPayment DelaySubscription TypeSupport CallsTenureTotal SpendUsage Frequency
Age1.0000.4320.122-0.1630.0660.0260.0510.0060.169-0.010-0.070-0.006
Churn0.4321.0000.4340.9490.1750.1720.4020.0200.6100.0780.4960.059
Contract Length0.1220.4341.0000.2910.0680.0470.1120.0070.1670.0230.1360.016
CustomerID-0.1630.9490.2911.0000.166-0.125-0.2430.013-0.4700.0440.3340.038
Gender0.0660.1750.0680.1661.0000.1560.0630.0030.0970.0110.0770.012
Last Interaction0.0260.1720.047-0.1250.1561.0000.0390.0010.075-0.007-0.052-0.005
Payment Delay0.0510.4020.112-0.2430.0630.0391.0000.0050.146-0.015-0.104-0.013
Subscription Type0.0060.0200.0070.0130.0030.0010.0051.0000.0080.0390.0070.000
Support Calls0.1690.6100.167-0.4700.0970.0750.1460.0081.000-0.027-0.199-0.021
Tenure-0.0100.0780.0230.0440.011-0.007-0.0150.039-0.0271.0000.017-0.027
Total Spend-0.0700.4960.1360.3340.077-0.052-0.1040.007-0.1990.0171.0000.017
Usage Frequency-0.0060.0590.0160.0380.012-0.005-0.0130.000-0.021-0.0270.0171.000

Missing values

2025-06-16T21:52:15.559995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-16T21:52:15.805663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CustomerIDAgeGenderTenureUsage FrequencySupport CallsPayment DelaySubscription TypeContract LengthTotal SpendLast InteractionChurn
0-1.740090-0.753327-1.1459110.448760-0.2105110.4545500.6096211.211543-1.1146611.2474280.2930521.0
1-1.7400822.059648-1.1459111.028279-1.7245652.083103-0.601319-1.2455590.001694-0.309865-0.9865851.0
2-1.7400741.255941-1.145911-1.000038-1.3751680.7802610.609621-1.2455591.118049-1.854700-1.3355761.0
3-1.7400671.4970530.8726680.3908080.6047491.105971-0.7224131.2115430.001694-0.9784631.6890201.0
4-1.740059-1.3159220.8726680.0430970.4882830.454550-0.601319-1.2455590.001694-0.0606980.6420441.0
5-1.7400440.9344580.8726680.1010491.0706111.7573921.578374-0.017008-1.114661-2.087256-0.7539231.0
6-1.7400361.497053-1.1459111.028279-0.443442-0.1968710.3674331.2115431.1180490.7864691.1073671.0
7-1.7400281.255941-1.1459110.332856-0.9093050.1288400.246339-0.017008-1.114661-0.7749771.8053511.0
8-1.740020-0.0299910.872668-1.115941-1.2587021.105971-1.0856951.2115431.1180491.401081-0.1722701.0
9-1.7400131.979278-1.145911-1.6375091.070611-0.522581-0.2380371.2115431.118049-0.8995601.6890201.0
CustomerIDAgeGenderTenureUsage FrequencySupport CallsPayment DelaySubscription TypeContract LengthTotal SpendLast InteractionChurn
4408231.7338730.6933460.872668-1.1738931.303543-0.8482920.6096211.211543-1.114661-0.055383-1.1029150.0
4408241.7338800.1307510.8726680.8544231.070611-0.196871-1.3278831.2115431.118049-0.0491120.0603910.0
4408251.7338880.1307510.872668-0.2466630.488283-0.522581-0.1169431.2115431.1180490.0106051.4563590.0
4408261.7338960.7737170.8726680.3328560.8376800.1288400.3674331.211543-1.1146610.1454871.8053510.0
4408271.7339030.4522340.872668-1.4636531.070611-0.5225810.246339-1.245559-1.1146610.852914-1.4519070.0
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